Data Our sample comprises 1,896 firm year observations for 327 firms that adopted IAS

4. Data Our sample comprises 1,896 firm year observations for 327 firms that adopted IAS

between 1994 and 2003 for which DataStream data are available from 1990 through 2003. Obtaining data beginning in 1990 provides us with a minimum of four years of pre-adoption period data. We obtain our sample of IAS firms from Worldscope, which identifies the set of

accounting standards a firm uses to prepare its financial statements. 21 We gather financial and accounting data from DataStream. The sample size reflects our having winsorized at the 5%

level all variables used to construct our metrics to mitigate the effects of outliers on our inferences.

In particular, the two Worldscope standards categories that we code as IAS based on the Worldscope Accounting Standards Applied data field are “international standards” and “IASC.” Daske et al. (2007) reports that this data field in Worldscope has classification error. However, any classification error in our study biases against finding differences in accounting quality in each of our comparisons.

Table 1, panel A, presents the country breakdown of our sample. In general, the sample is from many countries, with greatest representation from Switzerland, China, and Germany. 22

Panel B of table 1 presents the sample industry breakdown. The sample comprises a range of industries, with most in manufacturing, finance, insurance and real estate, or services. Panel C of table 1 presents a sample breakdown by IAS adoption year, and reveals variation across years.

Table 2 presents descriptive statistics relating to variables used in our analyses. Table 2 reveals that IAS firms have significantly fewer incidents of small positive earnings and

insignificantly more incidents of large negative earnings than do NIAS firms. 23 Although these descriptive statistics do not control for other factors, they suggest that IAS firms are less likely

than NIAS firms to manage earnings towards a target and more likely to recognize losses in a timely manner. In terms of control variables, although IAS firms have higher growth than do NIAS firms, the difference is not significant. Despite the size match, IAS firms are significantly larger than NIAS firms. Further, there is some evidence that IAS firms are more likely to issue debt (mean but not median difference is significant), more likely to issue equity (median but not mean difference is significant), and are less highly levered (mean but not median difference is significant). Relating to the last four control variables, on average, IAS firms trade on more exchanges than NIAS firms, are more likely to be audited by one of the large auditing firms, are more likely to list on a US stock exchange, and have a smaller percentage of closely held shares. All of these differences are significant.

22 Our sample of Chinese and German firms includes some firms that are required to apply IAS. These include Chinese B share firms and German New Market firms. We perform all of our comparisons omitting these firms.

None of the inferences differs from those obtained from the tabulated results. The table 1, panel A, country classification includes firms from the listed country that are incorporated off-shore, e.g., in Bermuda. The off-shore incorporation permits these firms to use IAS rather than domestic standards. For example, four UK firms are headquartered and operate in the UK, but are incorporated in Bermuda.

23 With the exception of the descriptive statistics in table 2 for which statistical significance is assessed using a two- sided alternative, throughout we use a 5% significance level to assess statistical significance based on a one-sided

alternative.